Method for localization of bone marrow white blood cells based on saturation clustering

ABSTRACT

A saturation clustering-based method for positioning bone marrow white blood cells: first, pre-processing a bone marrow white blood cell image to eliminate partial noise points and simultaneously smooth the image; using K-means clustering to cluster saturation channels of the bone marrow white blood cell image, and select the type of the white blood cells according to a decision tree algorithm; next, eliminating irrelevant areas in a binary image of the white blood cells by means of a morphology processing algorithm, and simultaneously filling in point holes in the white blood cells; and finally, positioning the white blood cells. The present method is simple and effective, and is suitable for a wide range of applications compared to existing threshold-based algorithms, while rendering a final result more accurate by integrating the decision tree algorithm.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims the priority of Chinese PatentApplication No. 201810495118.4, filed on May 22, 2018, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The invention belongs to the field of medical image processing and, moreparticularly, to a bone marrow white blood cell localization methodbased on saturation clustering.

BACKGROUND

There are various types of white blood cells in bone marrow, and thecolor difference of different types of white blood cells after stainingis also large. Compared with peripheral blood, the density of whiteblood cells in the bone marrow is greater, and some patients have celladhesions in the blood smear. Therefore, the positioning of bone marrowwhite blood cells has always been a challenging subject. In recentyears, experts and a large number of technical personnel have proposedmany effective solutions. However, most solutions can only solvespecific problems, and there is no general solution that can be appliedto most scenarios.

The current localization of bone marrow white blood cells is based on athreshold algorithm that separates white blood cells from the backgroundand red blood cells. For example, Wu et al., “A novel color imagesegmentation method and its application to white blood cell imageanalysis” (Signal Processing, 2006 8th International Conference on) usesan Ostu threshold algorithm to segment and locate white blood cells. Koet al., “Automatic white blood cell segmentation using stepwise mergingrules and gradient vector flow snake” (Micron, 2011, 42(7): 695-705)first uses a threshold algorithm to roughly estimate the location of thecell, and then uses the mean shift for further optimization. At the sametime, some scholars have proposed other effective schemes, such as themethod of applying morphological processing disclosed in the article“White blood cell segmentation using morphological operators andscale-space analysis” (Computer Graphics and Image Processing, 2007:294-304) by Dorini L B et al. There are also methods such as clustering.However, these methods have certain limitations. For example, in theOstu threshold algorithm, one of the assumptions is that the area of thebackground and the area of the foreground are roughly the same. Theactual bone marrow digital image may have a large proportion of whiteblood cells, or no white blood cells, and the color of the white bloodcells may be distributed over a large region, even overlapping withdarker red blood cells. Therefore, although the threshold can be appliedto most digital images, in some special cases this solution may notlocate white blood cells very well. The clustering algorithm may alsohave the same problem when the color distribution of white blood cellsis relatively scattered.

SUMMARY

The object of the present invention is to provide a method forlocalization of bone marrow leukocytes based on saturation clustering,which provides a white blood cell localization algorithm, which has ahigher density of white blood cells in the bone marrow, and a celladhesion phenomenon occurs in blood smears of some patients. The problemis that the area of white blood cells can be selected more precisely.

The present invention is achieved in this way, a method for locatingbone marrow white blood cells based on saturation clustering, comprisingthe following steps:

(1) median filtering the bone marrow white blood cell image to removesome noise;

(2) Color-changing the image of the bone marrow white blood cells,converting the image from the RGB (red, green and blue) channel to theHSV (color, saturation, brightness) channel;

(3) Apply the K-means algorithm to the S-saturation channel, divide itinto three parts, select the first part P1 or the first part of theP1+P2 part to get the white blood cell area. The following is theselection step;

(4) Calculate the average value (H1, H2) of the first two parts of the Hchannel in (3), and calculate the first two parts of (3) according tothe mean point (S1, S2) of the first two parts of (3) The area ratio ofthe area (ratio);

(5) Count the parts of the white blood cells in the multiple images, andrecord the values of H1−H2, S1−S2 and ratio when recording in the P1 orP2 part;

(6) According to the recorded results in (5), apply the decision treealgorithm to find out the conditions for making the choices;

(7) Morphologically processing the result of (6) to remove the unrelatedarea while filling the point hole;

(8) Position the white blood cells isolated in (7).

wherein in the step (3), a K-means algorithm is applied to the S(saturation) channel, and the method is divided into three. In part, thefirst part P1 is likely to be a white blood cell area, the second partP2 may be a red blood cell area or both red blood cells and white bloodcells, and the third part P3 is generally a background area, so onlyneed to select P1 or (P1+P2) part. Get the area of white blood cells.

wherein the average value (H1, H2) of the first two partial H channelsin (3) is calculated in the step (4). Calculate the mean point (S1, S2)of the first two parts of (3), and calculate the area ratio (ratio) ofthe first two parts of (3). The calculation formula of H1 is givenbelow:H1=Σ(P1.*H)/Σ(P1)H2=Σ(P2.*H)/Σ(P2)

Where P1 is a binary image, the pixel value belonging to the first partis 1, and the others are 0. a sum of P1 pixel values is Σ(p1),P1.*Hindicating a result of multiplying pixels at the same position;

Where P1 is a binary image, the pixel value belonging to the first partis 1, and the others are 0. a sum of P1 pixel values is Σ(p2),P2.*Hindicating a result of multiplying pixels at the same position.

wherein in the step (6), according to the recording result in (5), thedecision tree algorithm is applied to find out the rule formulation. Theselected condition, in which the loss function of the decision treealgorithm plus the number of leaf nodes, is used for pruning to preventoverfitting.

wherein in the step (7), the result of (6) is subjected to morphologicalprocessing to remove an unrelated region and fill the white blood cellregion. Point hole, the specific process is as follows: First, selectthe appropriate structure element b to do the corrosion operation on thebinary image obtained in (6), remove the unrelated area, and then do theexpansion operation,f=f⊖bf=f⊕b

And f is the binary image obtained in (6), which is an expansionoperation and is a corrosion operation;

Finally, the point holes in f are filled by morphologicalreconstruction;g=fD _(g) ⁽¹⁾(f)=(D _(g) ⁽⁰⁾ ⊕b)∩gD _(g) ^((n))(f)=D _(g) ⁽¹⁾(D _(g) ^((n−1))(f))

D_(g) ⁽¹⁾(f) is the result of a refactoring, ∩ and is.

Compared with the disadvantages and deficiencies of the prior art, thepresent invention has the following beneficial effects:

1. The algorithm of the invention is simple, effective and has a wideapplication range. Compared with the existing threshold-basedalgorithms, the algorithm of the present invention has strongeradaptability.

2. The problem of wide range of color distribution of different types ofwhite blood cells, as well as the dark color of red blood cells causedby dyeing. By combining the K-means algorithm and the decision treealgorithm of the patent of the present invention, the white blood cellregion can be selected more accurately.

Whether this part of the advantages can be more specifically describedfor the white blood cell positioning method used in the background art.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example of a bone marrow white blood cell image.

FIG. 2 is a median-filtered image of bone marrow white blood cells,according to an embodiment.

FIG. 3 is a three-part graph resulting from applying a K-means algorithmto an S channel of a color-converted image, according to an embodiment.

FIG. 4 is an image resulting from applying a decision tree algorithmselection, according to an embodiment.

FIG. 5 is an image resulting from removing irrelevant regions andfilling point holes, according to an embodiment.

FIG. 6 is an image resulting from locating white blood cells afterseparation, according to an embodiment.

FIG. 7 is a schematic block diagram of a device for locating bone marrowwhite blood cells, according to an embodiment.

DETAILED DESCRIPTION

The present application will be further described in detail below withreference to the accompanying drawings and embodiments. It is understoodthat the specific embodiments described herein are merely illustrativeof the invention and are not intended to limit the application.

A method for locating bone marrow white blood cells based on saturationclustering, includes the following steps.

In step (1), median filtering of a bone marrow white blood cell image isperformed to remove some noise. FIG. 1 is an example of a bone marrowwhite blood cell image, and the result of the media filtering of thebone marrow white blood cell image is shown in FIG. 2. In the medianfiltering, the size of a filtering template may be (5*5).

In step (2), color conversion of the median-filtered bone marrow whiteblood cell image obtained in step (1) is performed, to convert themedian-filtered image from an RGB (red, green, blue) channel to an HSV(color, saturation, brightness) channel. The specific formula used instep (2) is as follows:

V = max (R, G, B) $S = \left\{ {{\begin{matrix}{\frac{V - {\min\left( {R,G,B} \right)}}{V},} & {V \neq 0} \\{0,} & {V = 0}\end{matrix}H} = \left\{ \begin{matrix}{{60\left( {G - B} \right)\left( {V - {\min\left( {R,G,B} \right)}} \right)},} & {V = R} \\{{120 + {60\left( {B - R} \right)\left( {V - {\min\left( {R,G,B} \right)}} \right)}},} & {V = G} \\{{240 + {60\left( {R - G} \right)\left( {V - {\min\left( {R,G,B} \right)}} \right)}},} & {V = B}\end{matrix} \right.} \right.$

where the range of RGB values is [0, 1].

In step (3), a K-means algorithm is applied to the S (saturation)channel of the color-converted image, to divide the color-convertedimage into three parts. FIG. 3 is a three-part image resulting fromapplying the K-means algorithm to the S channel. As shown in FIG. 3, thefirst part P1 may be a white blood cell region, the second part P2 maybe a red blood cell region or a region including red blood cells andwhite blood cells, and the third part P3 may generally be a background.Therefore, only the first part P1 or the first and second parts P1+P2need to be selected to obtain the white blood cell area. The followingare the steps of selection.

In step (4), calculate the average values (H1, H2) of the H channel inthe first part P1 and the second part P2 in step (3), and calculate thearea ratio of first part P1 and the second part P2 in step (3) accordingto the mean points (S1, S2) of the first part P1 and the second part P2in step (3). The calculation formula of H1 and H2 is given below:H1=Σ(P1.*H)/Σ(P1)H2=Σ(P2.*H)/Σ(P2)

where P1 represents a binary image of the first part P1, the pixel valuebelonging to the first part P1 being 1, and the others being 0. Σ(P1)represents a sum of pixel values in the first part P1, and P1.*Hrepresents a result of multiplying values of pixels at the sameposition; and

where P2 represents a binary image of the second part P2, the pixelvalue belonging to the second part P2 being 1, and the others being 0.Σ(P2) represents a sum of pixel values in the second part P2, and P2.*Hrepresents a result of multiplying values of pixels at the sameposition.

In step (5), a statistical analysis is performed on multiple images toidentify first parts and second parts where white blood cells areincluded, and the values of H1−H2, S1−S2, and area ratios of theidentified first parts (P1) and second parts (P2) are recorded. Forexample, a statistics process is performed on 230 images. In 120 of the230 images, white blood cells are included in the first part (P1). In110 of the 230 images, white blood cells are included in the first partand second part (P1+P2). Images with no white blood cells are alsocollected.

In step (6), according to the recorded results in step (5), a decisiontree algorithm is applied to find out conditions for making selections.The loss function of the decision tree algorithm is added with thenumber of leaf nodes, to be used for pruning to prevent over-fitting.Then, selections are made on the color-converted image according to theconditions to obtain a binary image. After selection, the result isshown in FIG. 4.

In step (7), the result of step (6) is morphologically processed toremove irrelevant regions and fill point holes in a white blood cellregion in the morphologically processed image. The result is shown inFIG. 5. The specific process is as follows.

First, an appropriate structural element b is selected to perform anetching operation on the binary image obtained in (6), remove irrelevantregions, and then perform an expansion operation.f=f⊖bf=f⊕b

where f represents the binary image obtained in step (6), ⊕ representsan expansion operation, and ⊖ represents an etching operation;

Finally, the point holes in the image f are filled by morphologicalreconstruction.g=fD _(g) ⁽¹⁾(f)=(D _(g) ⁽⁰⁾ ⊕b)∩gD _(g) ^((n))(f)=D _(g) ⁽¹⁾(D _(g) ^((n−1))(f))

where D_(g) ⁽¹⁾(f) represents the result of a reconstruction, and ∩represents an AND operation.

After the morphologically processing and filling point holes, whiteblood cells are isolated from the image.

In step (8), the white blood cells isolated in step (7) are located, andthe results are shown in FIG. 6.

FIG. 7 is a block diagram of a device 700 for locating bone marrow whitecells, according to an embodiment. For example, the device 700 may be acomputer, a cloud server, and the like.

Referring to FIG. 7, the device 700 includes one or more of thefollowing components: a processor 702, a memory 704, a power component706, a multimedia component 708, an Input/Output (I/O) interface 710.

The processor 702 is configured to control overall operations of thedevice 700, such as the operations associated with locating bone marrowwhite cells. The processor 702 is configured to execute instructions toperform all or part of the disclosed methods. In some embodiments, theprocessor 702 includes a multimedia module configured to facilitate theinteraction between the multimedia component 708 and the processor 702.

The memory 704 is configured to store various types of data to supportthe operation of the device 700. Examples of such data includeinstructions for any applications or methods implemented by the device700, cell images, database, etc. The memory 704 may be implemented usingany type of volatile or non-volatile memory devices, or a combinationthereof, such as a static random access memory (SRAM), an electricallyerasable programmable read-only memory (EEPROM), an erasableprogrammable read-only memory (EPROM), a programmable read-only memory(PROM), a read-only memory (ROM), a magnetic memory, a flash memory, ora magnetic or optical disk.

The power component 706 is configured to provide power to variouscomponents of the device 700. The power component 706 includes a powermanagement system, one or more power sources, and any other componentsassociated with the generation, management, and distribution of power inthe device 700.

The multimedia component 708 includes a screen providing an outputinterface between the device 700 and a user of the device 700. In someembodiments, the screen may include a liquid crystal display and a presspanel.

The I/O interface 710 is configured to provide an interface for theprocessor 702 and peripheral interface modules, such as a keyboard, aclick wheel, buttons, and the like.

In some embodiments, the device 700 may be implemented with one or moreapplication specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), controllers, micro-controllers, microprocessors, or otherelectronic components, for performing the disclosed methods.

The present disclosure also provides a non-transitory computer-readablestorage medium including instructions, such as included in the memory704. The instructions are executable by the processor 702 of the device700, for performing the disclosed methods of locating bone marrow whitecells. For example, the non-transitory computer-readable storage mediummay be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disc, anoptical data storage device, and the like.

The above-described saturation clustering-based bone marrow white celllocalization method has the advantages of simple, effective, and wideapplication range. Compared with the existing threshold-based algorithm,the algorithm of the embodiment has stronger adaptability. Secondly, theapplication of K-means algorithm and decision tree algorithm can moreaccurately select the white blood cell area.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of thedisclosure disclosed here. This application is intended to cover anyvariations, uses, or adaptations of the disclosure following the generalprinciples thereof and including such departures from the presentdisclosure as come within known or customary practice in the art. It isintended that the specification and examples be considered as exemplaryonly, with a true scope and spirit of the disclosure being indicated bythe following claims.

It is to be understood that the present disclosure is not limited to theexact construction that has been described above and illustrated in theaccompanying drawings, and that various modifications and changes can bemade without departing form the scope thereof. It is intended that thescope of the disclosure only be limited by the appended claims.

What is claimed is:
 1. A method for locating bone marrow white bloodcells, comprising the steps of: (1) median filtering a bone marrow whiteblood cell image to remove some noise; (2) color-converting themedian-filtered bone marrow white blood cell image from an RGB (red,green and blue) channel to an HSV (color, saturation, brightness)channel; (3) applying a K-means algorithm to an S-saturation channel,dividing the color-converted image into three parts, and selecting afirst part or a first part and a second part to obtain a white bloodcell area; (4) calculating average values (H1, H2) of an H channel inthe first part and the second part obtained in step (3), and calculatingthe area ratio of the first part and the second part according to meanpoints (S1, S2) of the first part and the second part; (5) performing astatistical analysis on multiple images to identify first parts andsecond parts where white blood cells are included, and recording thevalues of H1−H2, S1−S2, and area ratios of the identified first partsand the second parts; (6) according to the recorded results in step (5),applying a decision tree algorithm to find out conditions for makingselections, and making selections on the color-converted image accordingto the conditions to obtain a binary image; (7) morphologicallyprocessing the result of step (6) to remove irrelevant regions, fillingpoint holes in a white blood cell region in the morphologicallyprocessed image, and isolating white blood cells from the image; and (8)locating the white blood cells isolated in step (7).
 2. The methodaccording to claim 1, wherein in the three parts in step (3), the firstpart is a white blood cell region, the second part is a red blood cellregion or a region including both red blood cells and white blood cells,and the third part is a background.
 3. The method according to claim 1,wherein the calculation formula of H1 is given below:H1=Σ(P1.*H)/Σ(P1)H2=Σ(P2.*H)/Σ(P2) where P1 represents a binary image of the first part,the pixel value belonging to the first part being 1, and the othersbeing 0, Σ(P1) represents a sum of pixel values in the first part, andP1.*H represents a result of multiplying values of pixels at the sameposition, and where P2 represents a binary image of the second part, thepixel value belonging to the second part being 1, and the others being0, Σ(P2) represents a sum of pixel values in the second part, and P2.*Hrepresents a result of multiplying values of pixels at the sameposition.
 4. The method according to claim 1, wherein in step (6), aloss function of the decision tree algorithm is added with the number ofleaf nodes, to be used for pruning to prevent over-fitting.
 5. Themethod according to claim 1, wherein step (7) includes: selecting anappropriate structure element b to perform an etching operation on thebinary image obtained in step (6), removing the irrelevant regions, andperforming an expansion operation, wherein the etching operation and theexpansion operation are represented by the following formula:f=f⊖bf=f⊕b where f represents the binary image obtained in step (6), ⊕represents an expansion operation, and ⊖ represents an etchingoperation; and filling the point holes in the image by morphologicalreconstruction, where the filing is represented by the followingformula:g=fD _(g) ⁽¹⁾(f)=(D _(g) ⁽⁰⁾ ⊕b)∩gD _(g) ^((n))(f)=D _(g) ⁽¹⁾(D _(g) ^((n−1))(f)) where D_(g) ⁽¹⁾(f)represents the result of a reconstruction, and ∩ represents an ANDoperation.